diff --git a/cGAN/data.py b/cGAN/data.py
index e1bcf84..e1a5706 100644
--- a/cGAN/data.py
+++ b/cGAN/data.py
@@ -73,7 +73,7 @@ def _image_decoder(path):
         else:
             dataset = tf.data.Dataset.from_tensor_slices(image_paths)
 
-        dataset = dataset.map(_parser)
+        dataset = dataset.map(_parser,num_parallel_calls=tf.data.experimental.AUTOTUNE)
         dataset = dataset.shuffle(buffer_size=8)
         dataset = dataset.batch(batch_size)
 
@@ -115,7 +115,7 @@ def _image_decoder(path):
             return image, semantic_map
 
         dataset = tf.data.Dataset.from_tensor_slices(image_paths, semantic_map_paths)
-        dataset = dataset.map(_parser)
+        dataset = dataset.map(_parser,num_parallel_calls=tf.data.experimental.AUTOTUNE)
         dataset = dataset.shuffle(buffer_size=8)
         dataset = dataset.batch(batch_size)
 
@@ -140,7 +140,7 @@ def _preprocess_inference(image_path, label, resize=(32,32)):
             return image, label
 
         dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
-        dataset = dataset.map(_preprocess_inference)
+        dataset = dataset.map(_preprocess_inference,num_parallel_calls=tf.data.experimental.AUTOTUNE)
         dataset = dataset.batch(batch_size)
         
         return dataset
diff --git a/data.py b/data.py
index d0e1f4e..7b15200 100644
--- a/data.py
+++ b/data.py
@@ -74,7 +74,7 @@ def _image_decoder(path):
             dataset = tf.data.Dataset.from_tensor_slices(image_paths)
 
         dataset = dataset.shuffle(buffer_size=8)
-        dataset = dataset.map(_parser)
+        dataset = dataset.map(_parser,num_parallel_calls=tf.data.experimental.AUTOTUNE)
         dataset = dataset.cache()
         dataset = dataset.batch(batch_size)
 
@@ -116,7 +116,7 @@ def _image_decoder(path):
             return image, semantic_map
 
         dataset = tf.data.Dataset.from_tensor_slices(image_paths, semantic_map_paths)
-        dataset = dataset.map(_parser)
+        dataset = dataset.map(_parser,num_parallel_calls=tf.data.experimental.AUTOTUNE)
         dataset = dataset.shuffle(buffer_size=8)
         dataset = dataset.batch(batch_size)
 
@@ -141,7 +141,7 @@ def _preprocess_inference(image_path, label, resize=(32,32)):
             return image, label
 
         dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
-        dataset = dataset.map(_preprocess_inference)
+        dataset = dataset.map(_preprocess_inference,num_parallel_calls=tf.data.experimental.AUTOTUNE)
         dataset = dataset.batch(batch_size)
         
         return dataset